Prevalence and influencing factors of hyperuricemia in middle-aged and older adults in the Yao minority area of China: a cross-sectional study

Hyperuricemia (HUA) endangers human health, and its prevalence has increased rapidly in recent decades. The current study investigated HUA's prevalence and influencing factors in Gongcheng, southern China. A cross-sectional investigation was conducted; 2128 participants aged 30–93 years were included from 2018 to 2019. Univariate and multivariate logistic regression models were used to screen HUA variables. A Bayesian network model was constructed using the PC algorithm to evaluate the association between influencing factors and HUA. The prevalence of HUA was 15.6% (23.2% in men, 10.7% in women). After screening the variables using a logistic regression analysis model, fatty liver disease (FLD), dyslipidemia, abdominal obesity, creatinine (CREA), somatotype, bone mass, drinking, and physical activity level at work were included in the Bayesian network model. The model results showed that dyslipidemia, somatotype, CREA, and drinking were directly related to HUA. Bone mass and FLD were indirectly associated with HUA by affecting the somatotype. The prevalence of HUA in Gongcheng was high in China. The prevalence of HUA was related to somatotype, drinking, bone mass, physical activity level at work, and other metabolic diseases. A good diet and moderate exercise are recommended to maintain a healthy somatotype and reduce the prevalence rate of HUA.

Definition. The HUA was defined as a serum UA level > 420 μmol/L in men and > 360 μmol/L in women 14 .
Bayesian network model. The Bayesian network model is a kind of probabilistic graph model that combines probability theory and graph theory to reveal the probabilistic dependence relationship between variables (nodes), which is represented by directed acyclic graph (DAG) 23 . In the DAG, the nodes represent the random variables U = {X 1 , ..., X n } , and the directed edges represent the direct probabilistic dependencies between the corresponding variables X 1 and X j .If there is an arc from X 1 to X j ,then we can informally say that X 1 causes X j , so X 1 and X j are often referred to as parent and child, respectively is used to quantitatively describe the strength of the relationship between a random variable and its parent. A Bayesian network is simply a representation of the joint probability distribution of a set of random variables X = {X 1 , ..., X n } , so the probability expression can be obtained: where π(X i ) represents the set of parent nodes of node X i , π(X i ) ⊆ {X 1 , ..., X i−1 } 24 . A conditional probability table (CPT) can describe the association strength between variables by constructing a DAG to reveal the potential relationship between influencing factors 24 . This can intuitively clarify the complex internal regulation relationship between diseases and their related factors to make up for some shortcomings of the logistic regression analysis model 25 .
Bayesian network learning algorithms. The learning of Bayesian network includes structure learning and parameter learning 26 . Structure learning is the process of constructing and determining the most suitable topological structure of Bayesian network from data, and its emphasis is to reveal the complex network relations among variables. Parameter learning is to determine the parameters of the model, the conditional probabilities and transition probabilities of the variables in the network, when the structure of the model is known. In this  www.nature.com/scientificreports/ paper, the PC algorithm in GeNle4.0 software is used for structure learning, and EM (expectation-maximization) algorithm is used for parameter learning. The PC structure learning algorithm is one of the earliest and the most popular algorithms, it uses independences observed in data (established by means of classical independence tests) to infer the structure that has generated them.
Statistical analysis. All data were collated using Microsoft Excel 2021. The normality of continuous variables was tested by using the Kolmogorov-Smirnov test. Data of all continuous variables that did not obey normality were presented as median and IQR. Categorical variables were described in percentages. Univariate analysis of categorical variables between HUA and non-HUA groups was performed using the Chi-square test and between creatinine (CREA) using the Mann-Whitney U test. The variables with statistical significance in univariate analysis were analyzed by binary logistic multivariate regression. The above statistical analysis was performed by SPSS28.0 software (IBM, Chicago, IL, USA) and 2-tailed P values < 0.05 were considered significant. The visualization of Fig. 1 was performed using QuickDraw software and the forest plot was visualized using GraphPad Prism 9.3.0 software shown in Fig. 2. The PC algorithm and EM algorithm of GeNle 4.0 Academic software were used to learn the structure and parameters of Bayesian network model, respectively. Bayesian network model graphs and CPTs were constructed using GeNle 4.0 Academic software as shown in Fig. 3.
Institutional review board statement. The study was conducted in accordance with the Declaration of Informed consent statement. Informed consent was obtained from all participants involved in the study.

Results
Basic demographic characteristics. A total of 2128 participants were included in this cross-sectional study, of whom 826 (33.3%) were men, 1302 (61.2%) were women, ranging in age from 30 to 93 years, with a mean age of 57.7 ± 12.0 years. The overall prevalence of HUA in this population was 15.6%, 23.2% in men and 10.7% in women, respectively (Table 1).

Univariate analysis.
The univariate analysis of the questionnaire data and physical examination data on the prevalence of HUA were shown in Table 1. The prevalence of HUA in men (826, 23.2%) was significantly higher than that of women (1302, 10.7%). Similarly, the educational level (≥ middle school) smoking, drinking, hypoxia, abdominal obesity and weekly working days (≤ 5 days) were risk factors for HUA. In addition, both oil tea and bone mass (osteopenia, osteoporosis) were protective factors against HUA in public. Non-working and obese showed the highest prevalence among the four physical activity level at work groups and somatotype group, respectively (P < 0.05). Interestingly, there were no significant differences between the HUA group and the non-HUA group in age, nation, occupation, household income, operation, medical insurance, daily oil/salt intake, green tea, walking time, and working time. Moreover, it was showed significant differences between the HUA group and the non-HUA group in other metabolic diseases (diabetes, FLD, dyslipidemia) and CREA level (P < 0.05). The occurrence of HUA was not related to hypertension and anemia (Table 1).  Table 2. The logistic regression analysis results showed that alcohol consumption, physical activity level at work, FLD, dyslipidemia, bone mass, abdominal obesity, somatotype, and CREA finally entered the model. Drinking, FLD, dyslipidemia, abdominal obesity, somatotype (overweight, obesity), and CREA were risk factors for HUA. In addition, physical activity level at work and bone mass (osteopenia) were protective factors against HUA.
Bayesian network model of HUA. According to the eight variables screened from the logistic regression analysis model, the Bayesian network model of related factors of HUA was further constructed using the EM algorithm in GeNle4.0 software. As shown in Fig. 2, a HUA Bayesian network model containing nine nodes and 14 directed edges was constructed. The results showed that drinking, dyslipidemia, somatotype, and CREA were directly related to HUA, in which drinking, dyslipidemia, and somatotype were the father nodes of HUA, and the child node of HUA was CREA. Bone mass and FLD were indirectly related to HUA by affecting somatotype, suggesting that somatotype was the intermediate variable between bone mass and FLD, affecting the occurrence of HUA, as shown in Fig. 2.
The calibration curve for predicting incidence and observed proportions closely followed the line of y = x. Additionally, the Hosmer-Lemeshow goodness of fit test yielded a P value of 0.633, greater than the significance level of 0.05. These results indicated that the Bayesian network model was well calibrated, as depicted in Fig. 3A. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) was found to be 0.812, which was greater than the cutoff value of 0.750. This suggested that the variables in the Bayesian network model had good discriminatory ability, as illustrated in Fig. 3B.  ing to the state of known nodes to determine the risk of HUA. The risk of HUA was the lowest (0.036) in patients with dyslipidemia and emaciation and in those who did not drink. The risk of HUA was highest when dyslipidemia, obesity, and drinking were present (0.773). The risk of developing HUA increased significantly with the change in somatotype from emaciation to obesity (Table 3).

Discussion
HUA has become a common metabolic disease, which is affected by economic development, environment, diet, race, heredity and other factors 27 The prevalence of HUA in China increased from 8.4 8 to 14.0% 5 during 2001-2019. In this cross-sectional study of adults from Gongcheng Yao Autonomous County, the prevalence rate of HUA was 15.6% (23.2% for men, 10.7% for women), corresponding to an estimated 38.3 thousand adults with HUA, which was higher than that reported in other neighboring Asian countries such as Japan (13.4%) 28 and Korea (11.4%) 29 . Moreover, the HUA prevalence in Gongcheng men (23.2%) was significantly higher than that found in some developed countries such as the USA 6 (20.2%) and Australia 30 (16.6%). The HUA prevalence in China is similar to that in developed countries.We hypothesized that the increased prevalence rates of HUA in Gongcheng may be related to China's rapid economic development and westernization of dietary habits in recent years 31 . It has been observed that HUA is more prevalent in individuals of Zhuang ethnicity compared to those of Han descent, with a reported prevalence of 24.5% in Guangxi in 2018-2019 11 . Furthermore, recent research has identified Zhuang descent as a risk factor for HUA 5 . However, in our study, the prevalence of HUA in Yao individuals was found to be lower (11.1%) than that in Han and Zhuang. Interestingly, even though the diet of www.nature.com/scientificreports/ Tibetan people is usually rich in meat, fat, and alcohol, the prevalence of HUA (2.05%) was still lower than that of Han (17.9%) 32 . Additionally, despite the Inner Mongolia Mongolian residents' diet primarily comprising meat and dairy products, the prevalence of HUA (10.0%) was also found to be lower in Mongo than in Han people 12 .
Major et al. 33 reported that genetic variants may play a greater role in hyperuricaemia in the general population compared with dietary exposure, which could explain the varying prevalence found in these studies. However, further research to confirm these findings is needed. Previous studies reported that the prevalence of HUA in men was higher than that in women 34,35 . In the current study, our results demonstrated sex differences in the prevalence of HUA, which was markedly higher in men (23.2%) than in women (10.7%). The results of the univariate analysis were significant. Such sex differences may be related to the higher estrogen level in women, which benefits UA excretion. In comparison, higher androgen level in men promotes renal reabsorption of UA and inhibits UA excretion 30,36 , owing to the lifestyle of men consisting of drinking and a high-fat and high-purine diet. However, the effect of sex was not significant after multivariate analysis, which may be owing to the small proportion of sex and other factors affecting HUA. This study further demonstrated that FLD, dyslipidemia, drinking, abdominal obesity, somatotype (overweight, obese), and CREA levels were all risk factors for HUA, which was consistent with the studies of other regions in China and with other ethnic groups [37][38][39][40] . Physical activity level at work and osteopenia were protective factors against HUA 5,41 . We speculated that the plasma volume increases with the increase in glomerular filtration rate and extracellular fluid volume during long-term moderate exercise, and the improvement in renal plasma flow would promote the secretion and excretion of UA 42 . We realize that the potential influencing factors analyzed in this study were limited; the investigation of more factors is warranted in future studies.
In addition, we found that the Bayesian network model diagram further demonstrated the complex network connection among the various influencing factors of HUA, among which dyslipidemia, somatotype, and drinking were directly related to HUA. Risk inference by the Bayesian network model showed that the risk of HUA was the highest in people with dyslipidemia and obesity and in those who drank. This might be because the lipotoxicity in dyslipidemia affects the function of islet β cells, increases the levels of free fatty acids, and promotes the occurrence of β-oxidation of free fatty acids; this enhances the activity of NADPH, promotes the synthesis of UA, and causes HUA 39 . The possible causes are visceral fat accumulation 36 , endocrine system disorder, androgen and ACTH level decrease, and UA excretion inhibition, which might lead to HUA complications 43 . The synthesis and metabolism of lactic acid would be accelerated during alcohol metabolism in those who consume alcohol, and lactic acid competitively inhibits UA secretion from renal tubules, activates the ion exchange function of the human urate anion exchanger, inhibits UA excretory function of the kidney, and stimulates UA reabsorption in proximal tubules 44,45 . In addition, people often consume purine-rich foods during drinking, which would further increase UA content and cause HUA 42 . At the same time, compared with the non-HUA group, the CREA level in the HUA group was significantly higher. Relevant studies 46,47 showed that the serum CREA level was the most commonly used renal function indicator, which might lead to chronic kidney disease (CKD). HUA can indirectly affect renal function by affecting the serum CREA level. It is worth noting that emaciated people with normal blood lipid levels had a lower risk of developing HUA. This may be because being emaciated and dyslipidemic may jointly contribute to a higher risk of developing HUA; however, further studies are needed in this regard. The results also suggested that bone mass and FLD were associated with the somatotype and that somatotype was directly associated with HUA. We assumed that might be because patients with osteopenia and FLD are mostly overweight and obese, thus promoting insulin resistance, reducing renal UA excretion, and leading to HUA 48 . www.nature.com/scientificreports/ Our study had several strengths. First, to our knowledge, this study is the first to investigate the prevalence of HUA in a large sample of Yao individuals, thereby providing valuable insights into the prevalence of this condition across different ethnic groups in China. Second, compared with previous studies on HUA, which used logistic and Cox regression models to describe several independent factors of HUA, the Bayesian network model could reveal how the factors were related to each other and affected the occurrence of HUA through the form of a probabilistic graphical model. This helped discover the potential influencing factors of the disease and provided new clues for further research. Third, this study was based on a survey of the natural population in ethnic minority areas. In addition to the physical examination data, a large amount of detailed questionnaire data was combined. The survey results had important practical significance for determining the prevalence of HUA in ethnic minority areas and specifying corresponding prevention and control strategies. However, some limitations of this study should be noted. First, most of the participants in this study were located in Gongcheng Yao Autonomous County, which might not be sufficient to represent the overall prevalence of HUA in Guangxi ethnic minorities. Second, as a cross-sectional study, the causal relationship between HUA and risk factors could not be determined, and further prospective studies are needed to demonstrate this.

Conclusions
In conclusion, this study has shown that the prevalence rates of HUA among adults in Gongcheng Yao Autonomous County during 2018-2019 were much higher than those reported in previous studies of the Chinese population and even higher than those found in some developed countries. The Bayesian network model can further supplement the complex network relationship among variables that cannot be displayed by the former and can more intuitively reveal the network relationship between diseases and related factors. This further suggests that the prevalence of HUA is influenced by a few factors, including somatotype, drinking, and other complicating metabolic diseases (such as FLD, dyslipidemia, and CKD). Interestingly, bone mass and physical activity level at work were independent protective factors against HUA. Thus, it is suggested to carry out health education for the population, guide the formation of a healthy lifestyle, and improve the blood UA level through good diet, alcohol restriction, adherence to moderate exercise, and maintaining a healthy and ideal somatotype to reduce the prevalence of HUA in the future.

Data availability
The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.